In [2]:
import os

## Set directory
os.chdir('/hpc/group/pbenfeylab/CheWei/CW_data/genesys')

import networkx as nx
from genesys_evaluate import *
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
# Suppress all warning messages
warnings.filterwarnings("ignore", category=DeprecationWarning)
In [3]:
## Conda Env pytorch-gpu on DCC
print(torch.__version__)
print(sc.__version__) 

1.13.0.post200
1.9.1
In [4]:
## Genes considered/used (shared among samples) 
gene_list = pd.read_csv('./gene_list_1108.csv')

Load Data¶

In [5]:
with open("./genesys_root_data.pkl", 'rb') as file_handle:
    data = pickle.load(file_handle)
    
batch_size = 2000
dataset = Root_Dataset(data['X_test'], data['y_test'])
loader = DataLoader(dataset,
                         batch_size = batch_size,
                         shuffle = True, drop_last=True)
In [6]:
input_size = data['X_train'].shape[1]
## 10 cell types 
output_size = 10
embedding_dim = 256
hidden_dim = 256
n_layers = 2
device = "cpu"
path = "./"

Load trained GeneSys model¶

In [7]:
model = ClassifierLSTM(input_size, output_size, embedding_dim, hidden_dim, n_layers).to(device)
model.load_state_dict(torch.load(path+"best_ALL_1130_continue.pth", map_location=torch.device('cpu')))
model = model
model.eval()
Out[7]:
ClassifierLSTM(
  (fc1): Sequential(
    (0): Linear(in_features=17513, out_features=256, bias=True)
    (1): Dropout(p=0.2, inplace=False)
    (2): GaussianNoise()
  )
  (fc): Sequential(
    (0): ReLU()
    (1): Linear(in_features=512, out_features=512, bias=True)
    (2): ReLU()
    (3): Linear(in_features=512, out_features=10, bias=True)
  )
  (lstm): LSTM(256, 256, num_layers=2, batch_first=True, dropout=0.2, bidirectional=True)
  (dropout): Dropout(p=0.2, inplace=False)
  (b_to_z): DBlock(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=512, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (bz2_infer_z1): DBlock(
    (fc1): Linear(in_features=1024, out_features=256, bias=True)
    (fc2): Linear(in_features=1024, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (z1_to_z2): DBlock(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=512, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (z_to_x): Decoder(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=256, out_features=256, bias=True)
    (fc3): Linear(in_features=256, out_features=17513, bias=True)
  )
)
In [8]:
classes = ['Columella', 'Lateral Root Cap', 'Phloem', 'Xylem', 'Procambium', 'Pericycle', 'Endodermis', 'Cortex', 'Atrichoblast', 'Trichoblast']
class2num = {c: i for (i, c) in enumerate(classes)}
num2class = {i: c for (i, c) in enumerate(classes)}
In [9]:
cts = ['Atrichoblast','Trichoblast','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Lateral Root Cap','Columella']
ctw = np.zeros((len(cts), 17513, 17513))
## number of cells sampled from the atlas
batch_size = 2000
In [ ]:
## GRN for the transition t0 to t1
for ct in cts:
    print(ct)
    cws = np.zeros((len(loader), 17513, 17513))
    with torch.no_grad():
        for i, sample in enumerate(loader):
            x = sample['x'].to(device)
            y = sample['y'].to(device)
            y_label = [num2class[i] for i in y.tolist()]
            
            pred_h = model.init_hidden(batch_size)
            tfrom = model.generate_current(x, pred_h, 0).to('cpu').detach().numpy()
            cfrom = tfrom[np.where(np.array(y_label)==ct)[0],:]
            
            pred_h = model.init_hidden(batch_size)
            tto = model.generate_next(x, pred_h, 1).to('cpu').detach().numpy()   
            cto = tto[np.where(np.array(y_label)==ct)[0],:]
            
            cw = torch.linalg.lstsq(torch.tensor(cfrom), torch.tensor(cto)).solution.detach().numpy()
            cws[i] = cw
    
    ## Calculate mean across number of repeats
    cwm = np.mean(cws, axis=0)
    ctw[cts.index(ct)] = cwm
In [11]:
# Save the array to disk
np.save('genesys_ctw_t0-t1.npy', ctw)
In [10]:
ctw = np.load('genesys_ctw_t0-t1.npy')
In [11]:
## Calculate z-scores
ctw_z = np.zeros((len(cts), 17513, 17513))
for i in range(len(cts)):
    ctw_z[i] = (ctw[i] - np.mean(ctw[i])) / np.std(ctw[i])
In [12]:
## Filtering based on z-scores (with no weights)
ctw_f = np.zeros((len(cts), 17513, 17513))
## z-score threshold (keep values > mean + threshold*std)
threshold=3
for i in range(len(cts)):
    ctw_f[i] = np.abs(ctw_z[i]) > threshold

Load TFs list¶

In [13]:
wanted_TFs = pd.read_csv("./Kay_TF_thalemine_annotations.csv")
In [14]:
## Make TF names unique and assign preferred names
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G33880"]="WOX9"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G45160"]="SCL27"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G04410"]="NAC78"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G29035"]="ORS1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02540"]="ZHD3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G16500"]="IAA26"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G09740"]="HAG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G24660"]="ZHD2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G46880"]="HDG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G28420"]="RLT1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G14580"]="BLJ"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G45260"]="BIB"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02070"]="RVN"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G28160"]="FIT"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G68360"]="GIS3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G20640"]="NLP4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G05550"]="VFP5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G59470"]="FRF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G15150"]="HAT7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G14750"]="WER"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G75710"]="BRON"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G74500"]="TMO7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G12646"]="RITF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G48100"]="ARR5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G16141"]="GATA17L"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G65640"]="NFL"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G62700"]="VND5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G36160"]="VND2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G66300"]="VND3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G12260"]="VND4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G62380"]="VND6"
In [15]:
pd.Series(wanted_TFs['Name']).value_counts().head(5)
Out[15]:
NAC001    1
PRE5      1
MYB118    1
MYB21     1
MYB0      1
Name: Name, dtype: int64

Network analysis¶

In [16]:
TFidx = []
for i in wanted_TFs['GeneID']:
    if i in gene_list['features'].tolist():
        TFidx.append(np.where(gene_list['features']==i)[0][0])

TFidx = np.sort(np.array(TFidx))
In [17]:
def network(i):
    ## No weights
    adj_nw = ctw_f[i]
    ## Weighted
    adj = ctw[i]*ctw_f[i]
    ## TF only
    adj = adj[np.ix_(TFidx,TFidx)]
    adj_nw = adj_nw[np.ix_(TFidx,TFidx)]
    
    ## Remove no connect 
    regidx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[0]).value_counts().index[pd.Series(np.where(adj_nw==True)[0]).value_counts()>=1]))
    taridx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[1]).value_counts().index[pd.Series(np.where(adj_nw==True)[1]).value_counts()>=1]))
    ## Reciprocol
    keepidx = np.sort(np.array(list(set(regidx).intersection(taridx))))
    #keepidx = np.sort(np.array(list(set(regidx).union(taridx))))
    
    TFID = np.array(gene_list['features'][TFidx])[keepidx].tolist()
    ## TF name to keep
    TFname = []
    for i in np.array(gene_list['features'][TFidx])[keepidx]:
        TFname.append(wanted_TFs['Name'][np.where(wanted_TFs['GeneID']==i)[0][0]])
        
    adj = adj[np.ix_(keepidx,keepidx)]
    
    # Create a NetworkX graph for non-directed edges
    G = nx.Graph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(i, j, weight=abs(weight), distance=1/abs(weight))
            

    ## Measures the extent to which how close a node is to all other nodes in the network, considering the shortest paths or geodesic distances between nodes
    closeness_centrality = nx.closeness_centrality(G, distance='distance')
    ## Measures the extent to which a node that are not only well-connected but also connected to other well-connected nodes.
    eigenvector_centrality = nx.eigenvector_centrality(G)
    
    # Create a NetworkX graph for diected edges
    G = nx.MultiDiGraph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(i, j, weight=weight)
    
    ## Measures the number of connections (edges) each node has
    degree_centrality = nx.degree_centrality(G)
    # Calculate outgoing centrality
    out_centrality = nx.out_degree_centrality(G)
    # Calculate incoming centrality
    in_centrality = nx.in_degree_centrality(G)
    ## Measures the extent to which a node lies on the shortest paths between other nodes.
    betweenness_centrality = nx.betweenness_centrality(G, weight='weight')
    
    ## Non_Reciprocal Out centrality
    # Visualize the graph
    pos = nx.spring_layout(G)  # Positions of the nodes
    
    # Node colors based on weighted betweenness centrality
    node_colors = [out_centrality[node] for node in G.nodes()]
    
    # Node sizes based on weighted betweenness centrality
    node_sizes = [out_centrality[node] * 1000 for node in G.nodes()]

    # Get the edge weights as a dictionary
    edge_weights = nx.get_edge_attributes(G, 'weight')
    edge_colors = ['red' if weight > 0 else 'blue' for (_, _, weight) in G.edges(data='weight')]
    
    # Scale the edge weights to desired linewidths
    max_weight = max(edge_weights.values())
    edge_widths = [float(edge_weights[edge]) / max_weight for edge in G.edges]
    
    # Draw the graph
    nx.draw(G, pos=pos, node_color=node_colors, node_size=node_sizes, with_labels=False, width=edge_widths, edge_color=edge_colors)
    # Add node labels
    labels = {node: G.nodes[node]['name'] for node in G.nodes}
    nx.draw_networkx_labels(G, pos=pos, labels=labels, font_size=8)
    
    # Add a colorbar to show the weighted betweenness centrality color mapping
    sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=min(node_colors), vmax=max(node_colors)))
    sm.set_array([])
    plt.colorbar(sm)
    
    # Show the plot
    plt.show()
    
    dc = pd.DataFrame.from_dict(degree_centrality, orient='index', columns=['degree_centrality'])
    oc = pd.DataFrame.from_dict(out_centrality, orient='index', columns=['out_centrality'])
    ic = pd.DataFrame.from_dict(in_centrality, orient='index', columns=['in_centrality'])
    bc = pd.DataFrame.from_dict(betweenness_centrality, orient='index', columns=['betweenness_centrality'])
    cc = pd.DataFrame.from_dict(closeness_centrality, orient='index', columns=['closeness_centrality'])
    ec = pd.DataFrame.from_dict(eigenvector_centrality, orient='index', columns=['eigenvector_centrality'])
    df = pd.concat([dc,oc,ic,bc,cc,ec], axis=1)
    df.index =TFname
    df = df.sort_values('betweenness_centrality', ascending=False)
    
    return(df)
In [18]:
atri = network(0)
In [19]:
atri
Out[19]:
degree_centrality out_centrality in_centrality betweenness_centrality closeness_centrality eigenvector_centrality
CRF2 0.972973 0.443694 0.529279 0.987778 0.001552 0.121887
HDA3 0.810811 0.085586 0.725225 0.987600 0.001709 0.116948
GATA2 1.157658 0.576577 0.581081 0.986390 0.001651 0.123640
NAC094 1.076577 0.693694 0.382883 0.972800 0.001532 0.118830
GATA4 0.752252 0.317568 0.434685 0.965210 0.001559 0.107537
... ... ... ... ... ... ...
TRFL3 0.049550 0.015766 0.033784 0.000000 0.000878 0.019458
TRP1 0.020270 0.002252 0.018018 0.000000 0.000704 0.008910
AT3G53680 0.038288 0.002252 0.036036 0.000000 0.000879 0.015820
AT5G58280 0.054054 0.047297 0.006757 0.000000 0.001022 0.018805
BPC5 0.011261 0.009009 0.002252 0.000000 0.000732 0.003928

445 rows × 6 columns

In [20]:
tri = network(1)
In [21]:
cor = network(2)
In [22]:
end = network(3)
In [23]:
per = network(4)
In [24]:
pro = network(5)
In [25]:
xyl = network(6)
In [26]:
phl = network(7)
In [27]:
lrc = network(8)
In [28]:
col = network(9)
In [29]:
atri.columns = ['atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality']
tri.columns = ['tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality']
cor.columns = ['cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality']
end.columns = ['end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality']
per.columns = ['per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality']
pro.columns = ['pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality']
xyl.columns = ['xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality']
phl.columns = ['phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality']
lrc.columns = ['lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality']
col.columns = ['col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality']
In [30]:
## Indentify main regulators in each net work
tff = []
tff = tff + atri[atri['atri_betweenness_centrality']>0].index.tolist()
tff = tff + tri[tri['tri_betweenness_centrality']>0].index.tolist()
tff = tff + lrc[lrc['lrc_betweenness_centrality']>0].index.tolist()
tff = tff + cor[cor['cor_betweenness_centrality']>0].index.tolist()
tff = tff + end[end['end_betweenness_centrality']>0].index.tolist()
tff = tff + per[per['per_betweenness_centrality']>0].index.tolist()
tff = tff + pro[pro['pro_betweenness_centrality']>0].index.tolist()
tff = tff + xyl[xyl['xyl_betweenness_centrality']>0].index.tolist()
tff = tff + phl[phl['phl_betweenness_centrality']>0].index.tolist()
tff = tff + col[col['col_betweenness_centrality']>0].index.tolist()
tf_occurance = pd.DataFrame(pd.Series(tff).value_counts(), columns=['tf_occurance'])
tf_spec = pd.concat([tf_occurance, atri, tri, lrc, cor, end, per, pro, xyl, phl, col], axis=1)
tf_spec = tf_spec.fillna(0)
In [31]:
## Epidermis (atri, tri, lrc)
celltype1='atri'
celltype2='tri'
celltype3='lrc'
ts = tf_spec[tf_spec['tf_occurance']==3][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==9].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[31]:
atri_betweenness_centrality tri_betweenness_centrality lrc_betweenness_centrality atri_out_centrality tri_out_centrality lrc_out_centrality atri_in_centrality tri_in_centrality lrc_in_centrality centrality_count centrality_sum
WRKY9 0.807150 0.048045 0.360050 0.015766 0.007782 0.010152 0.261261 0.085603 0.225888 9 10.821698
WRKY72 0.614285 0.160278 0.060996 0.119369 0.303502 0.005076 0.083333 0.108949 0.064721 9 10.520510
In [32]:
## atri, tri
celltype1='atri'
celltype2='tri'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[32]:
atri_betweenness_centrality tri_betweenness_centrality atri_out_centrality tri_out_centrality atri_in_centrality tri_in_centrality centrality_count centrality_sum
AT4G26810 0.702850 0.017783 0.045045 0.225681 0.304054 0.284047 6 7.579460
ORC1B 0.000031 0.000410 0.074324 0.151751 0.114865 0.163424 6 6.504805
In [33]:
## Atrichoblast specific
celltype = 'atri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[33]:
atri_betweenness_centrality atri_out_centrality atri_in_centrality centrality_count centrality_sum
LOL2 0.238357 0.121622 0.058559 3 3.418538
AT2G28450 0.118851 0.144144 0.150901 3 3.413896
IDD7 0.021257 0.256757 0.045045 3 3.323058
AT3G55080 0.020036 0.121622 0.060811 3 3.202469
AT1G27050 0.003050 0.090090 0.101351 3 3.194492
AT1G62150 0.000991 0.047297 0.087838 3 3.136127
In [34]:
## Trichoblast specific
celltype = 'tri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[34]:
tri_betweenness_centrality tri_out_centrality tri_in_centrality centrality_count centrality_sum
RHD6 0.754727 0.214008 0.015564 3 3.984299
AT2G37120 0.556922 0.023346 0.147860 3 3.728128
GRF2 0.136354 0.066148 0.035019 3 3.237521
DEL3 0.103797 0.159533 0.147860 3 3.411190
AT4G39160 0.074538 0.035019 0.023346 3 3.132904
MBD6 0.022176 0.241245 0.089494 3 3.352915
AT1G11490 0.003040 0.015564 0.019455 3 3.038059
NF-YC10 0.002994 0.023346 0.015564 3 3.041905
AT5G13780 0.000410 0.233463 0.120623 3 3.354496
AT1G61990 0.000152 0.264591 0.073930 3 3.338673
AT1G76110 0.000152 0.178988 0.085603 3 3.264743
In [35]:
## LRC specific
celltype = 'lrc'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[35]:
lrc_betweenness_centrality lrc_out_centrality lrc_in_centrality centrality_count centrality_sum
AT1G69030 0.832179 0.021574 0.086294 3 3.940047
SDG4 0.437516 0.137056 0.032995 3 3.607566
AT2G32030 0.131091 0.019036 0.017766 3 3.167893
HDG1 0.014527 0.027919 0.021574 3 3.064019
AT1G50620 0.011931 0.110406 0.065990 3 3.188327
HMGB4 0.003946 0.030457 0.048223 3 3.082626
BIM3 0.002491 0.001269 0.022843 3 3.026603
AT5G66770 0.001242 0.007614 0.041878 3 3.050734
BEH1 0.000905 0.041878 0.003807 3 3.046590
MYB3R-4 0.000493 0.090102 0.062183 3 3.152778
CIB5 0.000437 0.087563 0.040609 3 3.128610
WRKY7 0.000145 0.077411 0.046954 3 3.124511
JMJD5 0.000019 0.062183 0.022843 3 3.085045
RR10 0.000002 0.024112 0.022843 3 3.046956
In [36]:
## Columella specific
celltype = 'col'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[36]:
col_betweenness_centrality col_out_centrality col_in_centrality centrality_count centrality_sum
AXR3 0.780327 0.155063 0.116034 3 4.051424
AT5G23405 0.625950 0.006329 0.011603 3 3.643882
NTT 0.385629 0.085443 0.154008 3 3.625081
HMGB3 0.153477 0.033755 0.021097 3 3.208329
NAC016 0.111774 0.037975 0.047468 3 3.197217
AT2G42660 0.090067 0.044304 0.021097 3 3.155468
RGL3 0.075948 0.358650 0.170886 3 3.605484
SUVH9 0.070555 0.029536 0.002110 3 3.102200
WRI4 0.067721 0.017932 0.028481 3 3.114135
STOP1 0.055243 0.015823 0.031646 3 3.102712
ING1 0.047522 0.020042 0.003165 3 3.070729
BZR1 0.044636 0.112869 0.041139 3 3.198644
RAP2.4 0.036034 0.056962 0.132911 3 3.225908
AT2G45460 0.032864 0.026371 0.035865 3 3.095100
AT1G21580 0.014609 0.034810 0.035865 3 3.085284
KNAT3 0.006512 0.154008 0.094937 3 3.255457
COL5 0.005675 0.056962 0.110759 3 3.173397
BRM 0.005456 0.124473 0.091772 3 3.221701
ARF8 0.004796 0.118143 0.167722 3 3.290661
ARR5 0.003485 0.151899 0.046414 3 3.201798
CCA1 0.003160 0.039030 0.085443 3 3.127633
AT3G25790 0.002647 0.151899 0.062236 3 3.216782
AT1G76580 0.002521 0.011603 0.122363 3 3.136487
EICBP.B 0.002251 0.080169 0.036920 3 3.119340
AT1G70000 0.002163 0.101266 0.053797 3 3.157226
AT1G72030 0.002106 0.054852 0.024262 3 3.081220
AGL94 0.002106 0.023207 0.059072 3 3.084385
MAF5 0.001074 0.034810 0.040084 3 3.075968
NF-YB11 0.001055 0.018987 0.028481 3 3.048523
SIGE 0.000925 0.036920 0.011603 3 3.049448
SPL1 0.000833 0.068565 0.047468 3 3.116867
MBD7 0.000820 0.111814 0.032700 3 3.145335
AT2G17410 0.000731 0.045359 0.022152 3 3.068241
AT1G19000 0.000724 0.026371 0.022152 3 3.049247
SUVR1 0.000544 0.095992 0.025316 3 3.121852
ZFN1 0.000275 0.077004 0.029536 3 3.106815
IDD1 0.000256 0.053797 0.083333 3 3.137387
AT3G20010 0.000213 0.089662 0.058017 3 3.147892
RBR1 0.000188 0.093882 0.099156 3 3.193226
IAA14 0.000179 0.005274 0.001055 3 3.006508
AT3G57800 0.000137 0.015823 0.059072 3 3.075032
TTG1 0.000120 0.023207 0.022152 3 3.045479
ZFP6 0.000118 0.099156 0.063291 3 3.162565
AT3G06410 0.000104 0.051688 0.032700 3 3.084492
AGL20 0.000097 0.040084 0.050633 3 3.090814
CHR4 0.000030 0.072785 0.047468 3 3.120283
RR3 0.000030 0.088608 0.035865 3 3.124503
ERF3 0.000025 0.061181 0.054852 3 3.116058
HMG 0.000022 0.103376 0.055907 3 3.159305
AT4G00990 0.000019 0.047468 0.060127 3 3.107614
SWC2 0.000017 0.066456 0.015823 3 3.082295
PAT1 0.000013 0.069620 0.026371 3 3.096005
TLP7 0.000007 0.059072 0.063291 3 3.122370
CHC1 0.000002 0.064346 0.036920 3 3.101268
AT2G46810 0.000002 0.029536 0.007384 3 3.036922
CDC5 0.000002 0.072785 0.034810 3 3.107597
AT3G13350 0.000001 0.028481 0.013713 3 3.042195
In [37]:
## Ground tissue
celltype1='cor'
celltype2='end'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[37]:
cor_betweenness_centrality end_betweenness_centrality cor_out_centrality end_out_centrality cor_in_centrality end_in_centrality centrality_count centrality_sum
MYB36 0.989733 0.732886 0.043912 0.173789 0.411178 0.689459 6 9.040956
SCR 0.880072 0.301164 0.131737 0.227920 0.263473 0.433048 6 8.237414
MYB68 0.601681 0.180317 0.057884 0.153846 0.331337 0.527066 6 7.852131
JKD 0.749413 0.000049 0.031936 0.017094 0.357285 0.475783 6 7.631561
3xHMG-box1 0.060431 0.000016 0.375250 0.159544 0.127745 0.108262 6 6.831248
In [38]:
## Cortex specific
celltype = 'cor'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[38]:
cor_betweenness_centrality cor_out_centrality cor_in_centrality centrality_count centrality_sum
AT1G72210 0.304942 0.049900 0.221557 3 3.576399
HB4 0.234363 0.077844 0.043912 3 3.356120
AT5G05330 0.059206 0.141717 0.067864 3 3.268786
AGL65 0.001050 0.021956 0.035928 3 3.058934
In [39]:
## Endodermis specific
celltype = 'end'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[39]:
end_betweenness_centrality end_out_centrality end_in_centrality centrality_count centrality_sum
RTV1 0.285535 0.102564 0.025641 3 3.413740
AT5G41920 0.182084 0.079772 0.051282 3 3.313138
LHW 0.017403 0.136752 0.019943 3 3.174098
ABAP1 0.011860 0.122507 0.019943 3 3.154310
AT3G19360 0.002841 0.065527 0.051282 3 3.119650
AT5G59450 0.001718 0.111111 0.094017 3 3.206846
RAT5 0.000155 0.116809 0.088319 3 3.205283
RVN 0.000114 0.054131 0.242165 3 3.296410
In [40]:
## Stele
celltype1='per'
celltype2='pro'
celltype3='xyl'
celltype4='phl'
ts = tf_spec[tf_spec['tf_occurance']==4][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype4+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype4+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality', celltype4+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==12].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[40]:
per_betweenness_centrality pro_betweenness_centrality xyl_betweenness_centrality phl_betweenness_centrality per_out_centrality pro_out_centrality xyl_out_centrality phl_out_centrality per_in_centrality pro_in_centrality xyl_in_centrality phl_in_centrality centrality_count centrality_sum
AT3G43430 0.947658 0.979319 0.521720 0.757036 0.243848 0.835280 0.348449 0.697674 0.241611 0.501168 0.460621 0.526578 12 19.060961
TCP11 0.935218 0.833272 0.153327 0.509865 0.246085 0.235981 0.341289 0.134551 0.308725 0.294393 0.264916 0.222591 12 16.480214
AT5G51780 0.748658 0.005252 0.354427 0.577717 0.154362 0.297897 0.501193 0.397010 0.183445 0.275701 0.250597 0.239203 12 15.985462
AT5G50010 0.002237 0.685507 0.180317 0.759385 0.022371 0.098131 0.632458 0.202658 0.145414 0.279206 0.403341 0.315615 12 15.726639
AT4G24060 0.040835 0.195324 0.547607 0.592512 0.194631 0.117991 0.486874 0.383721 0.107383 0.196262 0.455847 0.260797 12 15.579783
AT1G12440 0.891208 0.101644 0.415531 0.027446 0.040268 0.021028 0.062053 0.141196 0.060403 0.157710 0.064439 0.107973 12 14.090900
GTL1 0.009009 0.085349 0.001170 0.558344 0.217002 0.197430 0.076372 0.204319 0.098434 0.136682 0.062053 0.303987 12 13.950151
HAT3 0.000010 0.044692 0.749575 0.047034 0.100671 0.109813 0.181384 0.240864 0.035794 0.072430 0.219570 0.147841 12 13.949678
AT3G51950 0.534846 0.015400 0.000034 0.002103 0.096197 0.108645 0.102625 0.161130 0.091723 0.148364 0.033413 0.164452 12 13.458932
In [41]:
## Pericycle
celltype = 'per'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[41]:
per_betweenness_centrality per_out_centrality per_in_centrality centrality_count centrality_sum
AT3G23690 0.685196 0.073826 0.055928 3 3.814950
GRF9 0.068679 0.277405 0.105145 3 3.451229
AT2G36340 0.047085 0.154362 0.064877 3 3.266325
VRN1 0.021569 0.082774 0.015660 3 3.120003
OFP1 0.005904 0.149888 0.073826 3 3.229617
AT4G19985 0.004620 0.078300 0.085011 3 3.167931
MYB25 0.002418 0.100671 0.076063 3 3.179151
SDG40 0.000020 0.058166 0.042506 3 3.100691
In [42]:
## Procambium
celltype = 'pro'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[42]:
pro_betweenness_centrality pro_out_centrality pro_in_centrality centrality_count centrality_sum
LBD16 0.772912 0.168224 0.429907 3 4.371043
HAT9 0.714341 0.080607 0.310748 3 4.105696
AT1G75490 0.466158 0.061916 0.114486 3 3.642560
AT4G20970 0.418462 0.108645 0.058411 3 3.585518
BZS1 0.416888 0.102804 0.117991 3 3.637682
AT2G20100 0.285058 0.157710 0.046729 3 3.489497
AT2G40200 0.272949 0.039720 0.148364 3 3.461033
AT1G03040 0.254993 0.064252 0.084112 3 3.403357
RAV2 0.202388 0.028037 0.154206 3 3.384631
MYBR1 0.199807 0.113318 0.094626 3 3.407751
DEL2 0.186957 0.036215 0.047897 3 3.271069
HB18 0.091779 0.011682 0.121495 3 3.224956
AT4G25410 0.074421 0.063084 0.056075 3 3.193580
LRP1 0.044317 0.011682 0.052570 3 3.108570
LBD29 0.024605 0.051402 0.068925 3 3.144932
SOM 0.021148 0.058411 0.065421 3 3.144980
bHLH104 0.014634 0.079439 0.078271 3 3.172344
AT5G61590 0.014088 0.054907 0.089953 3 3.158948
TEM1 0.011697 0.009346 0.179907 3 3.200950
MYB32 0.007378 0.059579 0.098131 3 3.165089
TRFL10 0.001168 0.028037 0.088785 3 3.117991
AT1G21000 0.001168 0.008178 0.078271 3 3.087617
AT2G35430 0.000243 0.057243 0.018692 3 3.076178
LBD39 0.000010 0.059579 0.051402 3 3.110991
In [43]:
## Xylem
celltype = 'xyl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[43]:
xyl_betweenness_centrality xyl_out_centrality xyl_in_centrality centrality_count centrality_sum
VND7 0.988467 0.281623 0.608592 3 4.878681
VND1 0.986548 0.527446 0.715990 3 5.229985
LBD18 0.984835 0.066826 0.646778 3 4.698439
VND4 0.973753 0.176611 0.503580 3 4.653944
JLO 0.968471 0.031026 0.591885 3 4.591383
MYB46 0.964052 0.372315 0.572792 3 4.909159
XND1 0.955002 0.568019 0.677804 3 5.200826
VND5 0.944536 0.152745 0.491647 3 4.588928
IAA6 0.913739 0.451074 0.677804 3 5.042617
AT5G03510 0.878818 0.090692 0.482100 3 4.451611
MYB83 0.846262 0.248210 0.465394 3 4.559866
ZHD3 0.813169 0.264916 0.505967 3 4.584052
AT1G66810 0.811918 0.105012 0.474940 3 4.391871
AT2G27930 0.795846 0.038186 0.295943 3 4.129975
FBH1 0.768816 0.042959 0.231504 3 4.043279
AT1G68200 0.766258 0.527446 0.279236 3 4.572941
NAC050 0.738886 0.119332 0.205251 3 4.063468
MYB60 0.687374 0.221957 0.195704 3 4.105035
ZFP5 0.617996 0.680191 0.417661 3 4.715848
HB17 0.610139 0.181384 0.088305 3 3.879829
DEAR3 0.603213 0.076372 0.145585 3 3.825170
BEE2 0.396610 0.267303 0.183771 3 3.847684
HB31 0.375512 0.434368 0.284010 3 4.093890
MYB50 0.346941 0.300716 0.293556 3 3.941213
TCP10 0.343875 0.415274 0.329356 3 4.088505
TLP1 0.338017 0.102625 0.083532 3 3.524175
HSL1 0.325182 0.138425 0.207637 3 3.671244
BZIP49 0.288320 0.219570 0.284010 3 3.791900
AT4G00940 0.274206 0.155131 0.136038 3 3.565376
AT1G26610 0.246349 0.238663 0.255370 3 3.740382
AT3G16350 0.170096 0.174224 0.188544 3 3.532865
SHP1 0.114684 0.255370 0.159905 3 3.529959
AT3G17100 0.106885 0.109785 0.045346 3 3.262016
AT1G24610 0.080049 0.047733 0.081146 3 3.208928
HYH 0.079444 0.276850 0.078759 3 3.435053
AT1G04990 0.048504 0.138425 0.128878 3 3.315807
TCP20 0.047156 0.365155 0.300716 3 3.713027
NST1 0.046682 0.463007 0.288783 3 3.798472
WLIM2a 0.040008 0.190931 0.210024 3 3.440962
HB34 0.026384 0.155131 0.200477 3 3.381993
AT3G10760 0.018397 0.121718 0.143198 3 3.283313
TLP2 0.013012 0.050119 0.088305 3 3.151437
JAZ12 0.007565 0.145585 0.152745 3 3.305895
ABF4 0.004847 0.095465 0.124105 3 3.224418
HB30 0.002387 0.028640 0.052506 3 3.083532
MYB4 0.002387 0.033413 0.178998 3 3.214797
AT2G44730 0.001953 0.190931 0.066826 3 3.259709
AT5G46910 0.001799 0.059666 0.097852 3 3.159316
ARR6 0.000805 0.150358 0.007160 3 3.158323
VND6 0.000131 0.057279 0.188544 3 3.245955
BZIP24 0.000126 0.241050 0.107399 3 3.348574
AT3G22100 0.000091 0.241050 0.257757 3 3.498898
OBP4 0.000034 0.171838 0.157518 3 3.329390
In [44]:
## Phloem
celltype = 'phl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[44]:
phl_betweenness_centrality phl_out_centrality phl_in_centrality centrality_count centrality_sum
APL 0.974834 0.272425 0.813953 3 5.061213
AT3G12730 0.964707 0.209302 0.644518 3 4.818528
AT2G03500 0.955810 0.280731 0.686047 3 4.922587
DOF6 0.949920 0.415282 0.622924 3 4.988126
AT5G41380 0.931761 0.194352 0.466777 3 4.592891
REM22 0.917618 0.421927 0.401993 3 4.741538
NAC020 0.909680 0.335548 0.498339 3 4.743567
NAC2 0.905457 0.192691 0.436877 3 4.535025
HCA2 0.902046 0.328904 0.509967 3 4.740916
NAC057 0.828547 0.333887 0.491694 3 4.654129
AT4G39780 0.805880 0.109635 0.338870 3 4.254385
DOF2.4 0.758932 0.445183 0.455150 3 4.659264
AT5G02460 0.686309 0.350498 0.403654 3 4.440462
GATA20 0.649167 0.109635 0.358804 3 4.117606
AT2G28810 0.638136 0.176080 0.456811 3 4.271027
KNAT2 0.545403 0.169435 0.295681 3 4.010520
AGL15 0.487521 0.186047 0.200997 3 3.874564
HK3 0.465387 0.098007 0.204319 3 3.767713
NAC086 0.432687 0.146179 0.212625 3 3.791491
BIM1 0.424392 0.098007 0.122924 3 3.645323
KNAT6 0.262763 0.202658 0.137874 3 3.603294
LBD38 0.214374 0.142857 0.196013 3 3.553245
BRH1 0.185817 0.184385 0.142857 3 3.513060
CDF3 0.174908 0.230897 0.156146 3 3.561951
AT4G17900 0.168725 0.093023 0.167774 3 3.429522
ARR4 0.106138 0.240864 0.104651 3 3.451653
WRKY39 0.090939 0.058140 0.187708 3 3.336786
LBD37 0.071973 0.265781 0.106312 3 3.444066
HSFA7A 0.069466 0.129568 0.098007 3 3.297041
TLP9 0.068886 0.026578 0.197674 3 3.293138
AT1G47570 0.062158 0.157807 0.111296 3 3.331261
BBX21 0.046993 0.169435 0.098007 3 3.314434
MCC1 0.015191 0.200997 0.094684 3 3.310872
AT5G57150 0.010807 0.102990 0.084718 3 3.198515
MYB55 0.002971 0.101329 0.157807 3 3.262107
SPL2 0.001542 0.083056 0.019934 3 3.104532
ARF4 0.001412 0.122924 0.076412 3 3.200748
bZIP52 0.001302 0.139535 0.099668 3 3.240504
SVP 0.000837 0.081395 0.230897 3 3.313130
VOZ1 0.000824 0.104651 0.265781 3 3.371256
NAC045 0.000768 0.071429 0.139535 3 3.211732
HMGB1 0.000572 0.098007 0.041528 3 3.140107
RAP2.7 0.000357 0.164452 0.098007 3 3.262815
NAC080 0.000053 0.068106 0.064784 3 3.132943
AT3G16940 0.000030 0.114618 0.121262 3 3.235911

Search for individual genes¶

In [45]:
gene = 'SHR'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[45]:
tf_occurance cor_degree_centrality cor_out_centrality cor_in_centrality cor_betweenness_centrality cor_closeness_centrality cor_eigenvector_centrality end_degree_centrality end_out_centrality end_in_centrality ... xyl_in_centrality xyl_betweenness_centrality xyl_closeness_centrality xyl_eigenvector_centrality phl_degree_centrality phl_out_centrality phl_in_centrality phl_betweenness_centrality phl_closeness_centrality phl_eigenvector_centrality
SHR 6.0 0.209581 0.169661 0.03992 0.737369 0.001846 0.047194 0.148148 0.116809 0.031339 ... 0.221957 0.138545 0.001492 0.073361 0.280731 0.159468 0.121262 0.179678 0.001054 0.043674

1 rows × 37 columns

In [46]:
gene = 'BLJ'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[46]:
end_degree_centrality end_out_centrality end_in_centrality end_closeness_centrality end_eigenvector_centrality
BLJ 0.279202 0.014245 0.264957 0.001093 0.06801
In [47]:
gene = 'JKD'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[47]:
tf_occurance cor_degree_centrality cor_out_centrality cor_in_centrality cor_betweenness_centrality cor_closeness_centrality cor_eigenvector_centrality end_degree_centrality end_out_centrality end_in_centrality end_betweenness_centrality end_closeness_centrality end_eigenvector_centrality col_degree_centrality col_out_centrality col_in_centrality col_closeness_centrality col_eigenvector_centrality
JKD 2.0 0.389222 0.031936 0.357285 0.749413 0.002287 0.073996 0.492877 0.017094 0.475783 0.000049 0.001144 0.090325 0.103376 0.037975 0.065401 0.000453 0.037795
In [48]:
gene = 'RVN'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[48]:
tf_occurance cor_degree_centrality cor_out_centrality cor_in_centrality cor_closeness_centrality cor_eigenvector_centrality end_degree_centrality end_out_centrality end_in_centrality end_betweenness_centrality end_closeness_centrality end_eigenvector_centrality col_degree_centrality col_out_centrality col_in_centrality col_closeness_centrality col_eigenvector_centrality
RVN 1.0 0.151697 0.013972 0.137725 0.002143 0.039004 0.296296 0.054131 0.242165 0.000114 0.000935 0.068941 0.024262 0.016878 0.007384 0.000377 0.016152
In [49]:
gene = 'BIB'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[49]:
end_degree_centrality end_out_centrality end_in_centrality end_closeness_centrality end_eigenvector_centrality
BIB 0.219373 0.025641 0.193732 0.001052 0.055765
In [50]:
gene = 'IME'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[50]:
In [51]:
gene = 'MYB66'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[51]:
In [52]:
gene = 'GL2'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[52]:
tf_occurance atri_degree_centrality atri_out_centrality atri_in_centrality atri_betweenness_centrality atri_closeness_centrality atri_eigenvector_centrality tri_degree_centrality tri_out_centrality tri_in_centrality ... cor_out_centrality cor_in_centrality cor_betweenness_centrality cor_closeness_centrality cor_eigenvector_centrality end_degree_centrality end_out_centrality end_in_centrality end_closeness_centrality end_eigenvector_centrality
GL2 4.0 0.470721 0.132883 0.337838 0.884179 0.001421 0.088018 0.373541 0.280156 0.093385 ... 0.129741 0.015968 0.006942 0.001566 0.037103 0.011396 0.005698 0.005698 0.00059 0.004701

1 rows × 30 columns

In [53]:
tf_spec.to_csv('TF_GRN_centrality_t0-t1_zscore3.csv', index=True)
In [ ]:
In [ ]: